Learning Antidote Data to Individual Unfairness

29 November 2022

Papers citing "Learning Antidote Data to Individual Unfairness"

9 / 9 papers shown

Title
LayerMatch: Do Pseudo-labels Benefit All Layers? Chaoqi Liang Guanglei Yang Lifeng Qiao Zitong Huang Hongliang Yan Yunchao Wei W. Zuo 36 0 0 20 Jun 2024
AIM: Attributing, Interpreting, Mitigating Data Unfairness Zhining Liu Ruizhong Qiu Zhichen Zeng Yada Zhu Hendrik Hamann Hanghang Tong FaML 31 4 0 13 Jun 2024
On the Maximal Local Disparity of Fairness-Aware Classifiers Jinqiu Jin Haoxuan Li Fuli Feng 42 3 0 05 Jun 2024
Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Models Anshuman Chhabra Bo Li Jian Chen Prasant Mohapatra Hongfu Liu TDI 18 0 0 06 May 2024
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers Wencong You Zayd Hammoudeh Daniel Lowd AAML 11 12 0 28 Oct 2023
On the Cause of Unfairness: A Training Sample Perspective Yuanshun Yao Yang Liu TDI 31 0 0 30 Jun 2023
Achieving Fairness at No Utility Cost via Data Reweighing with Influence Peizhao Li Hongfu Liu TDI 25 45 0 01 Feb 2022
Disentangling Adversarial Robustness and Generalization David Stutz Matthias Hein Bernt Schiele AAML OOD 186 272 0 03 Dec 2018
Constructing Unrestricted Adversarial Examples with Generative Models Yang Song Rui Shu Nate Kushman Stefano Ermon GAN AAML 174 302 0 21 May 2018